249 results on '"Figueiras-Vidal, Aníbal R."'
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2. Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles
3. Imbalance example-dependent cost classification: A Bayesian based method
4. Double-Layer Stacked Denoising Autoencoders for Regression
5. On the design of Bayesian principled algorithms for imbalanced classification
6. Double-Layer Stacked Denoising Autoencoders for Regression
7. Complete autoencoders for classification with missing values
8. Machine-Health Application Based on Machine Learning Techniques for Prediction of Valve Wear in a Manufacturing Plant
9. A Principled Two-Step Method for Example-Dependent Cost Binary Classification
10. MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. Ensembles overview and proposal
11. Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification
12. Designing non-linear minimax and related discriminants by disjoint tangent configurations applied to RBF networks
13. Complete Stacked Denoising Auto-Encoders for Regression
14. Exploiting label information to improve auto-encoding based classifiers
15. Improving deep learning performance with missing values via deletion and compensation
16. Likelihood ratio equivalence and imbalanced binary classification
17. Values Deletion to Improve Deep Imputation Processes
18. Pre-emphasizing Binarized Ensembles to Improve Classification Performance
19. Training neural network classifiers through Bayes risk minimization applying unidimensional Parzen windows
20. On building ensembles of stacked denoising auto-encoding classifiers and their further improvement
21. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets
22. Machine-Health Application Based on Machine Learning Techniques for Prediction of Valve Wear in a Manufacturing Plant
23. A Principled Two-Step Method for Example-Dependent Cost Binary Classification
24. Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks
25. Post-aggregation of classifier ensembles
26. A new boosting design of Support Vector Machine classifiers
27. Smoothed Emphasis for Boosting Ensembles
28. Pre-emphasizing Binarized Ensembles to Improve Classification Performance
29. Values Deletion to Improve Deep Imputation Processes
30. Multi-task Neural Networks for Dealing with Missing Inputs
31. A Single Layer Perceptron Approach to Selective Multi-task Learning
32. Channel Equalization with Neural Networks
33. Exploiting Multitask Learning Schemes Using Private Subnetworks
34. Classifying patterns with missing values using Multi-Task Learning perceptrons
35. Support Vector Method for ARMA System Identification: A Robust Cost Interpretation
36. Support Vector Robust Algorithms for Non- parametric Spectral Analysis
37. Multi-dimensional Function Approximation and Regression Estimation
38. Neighborhood Guided Smoothed Emphasis for Real Adaboost Ensembles
39. Boundary Methods for Distribution Analysis
40. Digital Equalization Using Modular Neural Networks: an Overview
41. Walsh Sequences as Direct Error Correcting Ouput Code Dichotomies for Multiclass Problems
42. Imbalance Example-Dependent Cost Classification: A Bayesian Based Method
43. Optimal blind equalization of Gaussian channels
44. Nonlinear time series modeling by competitive segmentation of state space
45. Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks
46. Committees of Adaboost ensembles with modified emphasis functions
47. K nearest neighbours with mutual information for simultaneous classification and missing data imputation
48. Pattern classification with missing data: a review
49. Asymmetric label switching resists binary imbalance
50. Corrigendum to “Likelihood ratio equivalence and imbalanced binary classification” [Expert Systems with Applications, Volume 130 (2019), Pages 84--96]
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